{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multiclass Classification of White Wine Quality\n",
    "\n",
    "## Wine Data\n",
    "Data from http://archive.ics.uci.edu/ml/datasets/Wine+Quality\n",
    "\n",
    "### Citations\n",
    "P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis. \n",
    "Modeling wine preferences by data mining from physicochemical properties.\n",
    "In Decision Support Systems, Elsevier, 47(4):547-553. ISSN: 0167-9236.\n",
    "\n",
    "Available at:\n",
    "- [@Elsevier](http://dx.doi.org/10.1016/j.dss.2009.05.016)\n",
    "- [Pre-press (pdf)](http://www3.dsi.uminho.pt/pcortez/winequality09.pdf)\n",
    "- [bib](http://www3.dsi.uminho.pt/pcortez/dss09.bib)\n",
    "\n",
    "Dua, D. and Karra Taniskidou, E. (2017). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.\n",
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "white_wine = pd.read_csv('../../ch_09/data/winequality-white.csv', sep=';')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.0</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.36</td>\n",
       "      <td>20.7</td>\n",
       "      <td>0.045</td>\n",
       "      <td>45.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1.0010</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.45</td>\n",
       "      <td>8.8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.3</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.049</td>\n",
       "      <td>14.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>0.9940</td>\n",
       "      <td>3.30</td>\n",
       "      <td>0.49</td>\n",
       "      <td>9.5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.1</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.40</td>\n",
       "      <td>6.9</td>\n",
       "      <td>0.050</td>\n",
       "      <td>30.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>0.9951</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.44</td>\n",
       "      <td>10.1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.2</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.32</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.058</td>\n",
       "      <td>47.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>0.9956</td>\n",
       "      <td>3.19</td>\n",
       "      <td>0.40</td>\n",
       "      <td>9.9</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.2</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.32</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.058</td>\n",
       "      <td>47.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>0.9956</td>\n",
       "      <td>3.19</td>\n",
       "      <td>0.40</td>\n",
       "      <td>9.9</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "0            7.0              0.27         0.36            20.7      0.045   \n",
       "1            6.3              0.30         0.34             1.6      0.049   \n",
       "2            8.1              0.28         0.40             6.9      0.050   \n",
       "3            7.2              0.23         0.32             8.5      0.058   \n",
       "4            7.2              0.23         0.32             8.5      0.058   \n",
       "\n",
       "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "0                 45.0                 170.0   1.0010  3.00       0.45   \n",
       "1                 14.0                 132.0   0.9940  3.30       0.49   \n",
       "2                 30.0                  97.0   0.9951  3.26       0.44   \n",
       "3                 47.0                 186.0   0.9956  3.19       0.40   \n",
       "4                 47.0                 186.0   0.9956  3.19       0.40   \n",
       "\n",
       "   alcohol  quality  \n",
       "0      8.8        6  \n",
       "1      9.5        6  \n",
       "2     10.1        6  \n",
       "3      9.9        6  \n",
       "4      9.9        6  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "white_wine.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4898.000000</td>\n",
       "      <td>4898.000000</td>\n",
       "      <td>4898.000000</td>\n",
       "      <td>4898.000000</td>\n",
       "      <td>4898.000000</td>\n",
       "      <td>4898.000000</td>\n",
       "      <td>4898.000000</td>\n",
       "      <td>4898.000000</td>\n",
       "      <td>4898.000000</td>\n",
       "      <td>4898.000000</td>\n",
       "      <td>4898.000000</td>\n",
       "      <td>4898.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.854788</td>\n",
       "      <td>0.278241</td>\n",
       "      <td>0.334192</td>\n",
       "      <td>6.391415</td>\n",
       "      <td>0.045772</td>\n",
       "      <td>35.308085</td>\n",
       "      <td>138.360657</td>\n",
       "      <td>0.994027</td>\n",
       "      <td>3.188267</td>\n",
       "      <td>0.489847</td>\n",
       "      <td>10.514267</td>\n",
       "      <td>5.877909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.843868</td>\n",
       "      <td>0.100795</td>\n",
       "      <td>0.121020</td>\n",
       "      <td>5.072058</td>\n",
       "      <td>0.021848</td>\n",
       "      <td>17.007137</td>\n",
       "      <td>42.498065</td>\n",
       "      <td>0.002991</td>\n",
       "      <td>0.151001</td>\n",
       "      <td>0.114126</td>\n",
       "      <td>1.230621</td>\n",
       "      <td>0.885639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.800000</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.009000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.987110</td>\n",
       "      <td>2.720000</td>\n",
       "      <td>0.220000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.300000</td>\n",
       "      <td>0.210000</td>\n",
       "      <td>0.270000</td>\n",
       "      <td>1.700000</td>\n",
       "      <td>0.036000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>108.000000</td>\n",
       "      <td>0.991723</td>\n",
       "      <td>3.090000</td>\n",
       "      <td>0.410000</td>\n",
       "      <td>9.500000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.800000</td>\n",
       "      <td>0.260000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>5.200000</td>\n",
       "      <td>0.043000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>134.000000</td>\n",
       "      <td>0.993740</td>\n",
       "      <td>3.180000</td>\n",
       "      <td>0.470000</td>\n",
       "      <td>10.400000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.300000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>0.390000</td>\n",
       "      <td>9.900000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>167.000000</td>\n",
       "      <td>0.996100</td>\n",
       "      <td>3.280000</td>\n",
       "      <td>0.550000</td>\n",
       "      <td>11.400000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>14.200000</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>1.660000</td>\n",
       "      <td>65.800000</td>\n",
       "      <td>0.346000</td>\n",
       "      <td>289.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>1.038980</td>\n",
       "      <td>3.820000</td>\n",
       "      <td>1.080000</td>\n",
       "      <td>14.200000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       fixed acidity  volatile acidity  citric acid  residual sugar  \\\n",
       "count    4898.000000       4898.000000  4898.000000     4898.000000   \n",
       "mean        6.854788          0.278241     0.334192        6.391415   \n",
       "std         0.843868          0.100795     0.121020        5.072058   \n",
       "min         3.800000          0.080000     0.000000        0.600000   \n",
       "25%         6.300000          0.210000     0.270000        1.700000   \n",
       "50%         6.800000          0.260000     0.320000        5.200000   \n",
       "75%         7.300000          0.320000     0.390000        9.900000   \n",
       "max        14.200000          1.100000     1.660000       65.800000   \n",
       "\n",
       "         chlorides  free sulfur dioxide  total sulfur dioxide      density  \\\n",
       "count  4898.000000          4898.000000           4898.000000  4898.000000   \n",
       "mean      0.045772            35.308085            138.360657     0.994027   \n",
       "std       0.021848            17.007137             42.498065     0.002991   \n",
       "min       0.009000             2.000000              9.000000     0.987110   \n",
       "25%       0.036000            23.000000            108.000000     0.991723   \n",
       "50%       0.043000            34.000000            134.000000     0.993740   \n",
       "75%       0.050000            46.000000            167.000000     0.996100   \n",
       "max       0.346000           289.000000            440.000000     1.038980   \n",
       "\n",
       "                pH    sulphates      alcohol      quality  \n",
       "count  4898.000000  4898.000000  4898.000000  4898.000000  \n",
       "mean      3.188267     0.489847    10.514267     5.877909  \n",
       "std       0.151001     0.114126     1.230621     0.885639  \n",
       "min       2.720000     0.220000     8.000000     3.000000  \n",
       "25%       3.090000     0.410000     9.500000     5.000000  \n",
       "50%       3.180000     0.470000    10.400000     6.000000  \n",
       "75%       3.280000     0.550000    11.400000     6.000000  \n",
       "max       3.820000     1.080000    14.200000     9.000000  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "white_wine.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4898 entries, 0 to 4897\n",
      "Data columns (total 12 columns):\n",
      "fixed acidity           4898 non-null float64\n",
      "volatile acidity        4898 non-null float64\n",
      "citric acid             4898 non-null float64\n",
      "residual sugar          4898 non-null float64\n",
      "chlorides               4898 non-null float64\n",
      "free sulfur dioxide     4898 non-null float64\n",
      "total sulfur dioxide    4898 non-null float64\n",
      "density                 4898 non-null float64\n",
      "pH                      4898 non-null float64\n",
      "sulphates               4898 non-null float64\n",
      "alcohol                 4898 non-null float64\n",
      "quality                 4898 non-null int64\n",
      "dtypes: float64(11), int64(1)\n",
      "memory usage: 459.2 KB\n"
     ]
    }
   ],
   "source": [
    "white_wine.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'quality score')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = white_wine.quality.value_counts().sort_index(\n",
    "    ascending=False\n",
    ").plot.barh(title='White Wine Quality Scores', figsize=(12, 3))\n",
    "for bar in ax.patches:\n",
    "    ax.text(\n",
    "        bar.get_width(), \n",
    "        bar.get_y() + bar.get_height()/4, \n",
    "        f'{bar.get_width()/white_wine.shape[0]:.1%}'\n",
    "    )\n",
    "plt.xlabel('count of wines')\n",
    "plt.ylabel('quality score')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## White wine quality multi-class classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\molinstefanie\\packt\\venv\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:758: ConvergenceWarning: lbfgs failed to converge. Increase the number of iterations.\n",
      "  \"of iterations.\", ConvergenceWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "y = white_wine.quality\n",
    "X = white_wine.drop(columns=['quality'])\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, test_size=0.25, random_state=0, stratify=y\n",
    ")\n",
    "\n",
    "lm_pipeline = Pipeline([\n",
    "    ('scale', StandardScaler()), \n",
    "    ('lm', LogisticRegression(solver='lbfgs', multi_class='multinomial', random_state=0))\n",
    "]).fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "preds = lm_pipeline.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           3       1.00      0.20      0.33         5\n",
      "           4       0.88      0.17      0.29        41\n",
      "           5       0.59      0.54      0.56       364\n",
      "           6       0.53      0.76      0.62       550\n",
      "           7       0.54      0.21      0.31       220\n",
      "           8       0.00      0.00      0.00        44\n",
      "           9       0.00      0.00      0.00         1\n",
      "\n",
      "   micro avg       0.55      0.55      0.55      1225\n",
      "   macro avg       0.50      0.27      0.30      1225\n",
      "weighted avg       0.54      0.55      0.51      1225\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\molinstefanie\\packt\\venv\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "c:\\users\\molinstefanie\\packt\\venv\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "c:\\users\\molinstefanie\\packt\\venv\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test, preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x136e4b0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TtkixJz33KEsXzqdSlaqMf+l9APbv28vLT4xhV9o2aiTU5s4HxhNfsRIAq5cv5r0Jz5KVdZSKlaow6slXin3+eW3auIEH7h2eu741ZQt33HV3UP/Nc4T6s5bX/LlzePKJ8WRnZdO3X39uuW1g4W8KgkiqwfmS4B4u6sHdWe334Vy7O6qqrYt6rILExsUy4t4HOKV5CzIy9nP1Ff04r80FNGrUONChnHixcdw9/D6andKcjIwMbrzmcs4593y++PxTzj7nPK6/+Tbeev013npjIoOHjih2vHrndKbRhT1Y9N6zuduWfPAfWvW6mZqNW7FpwTes++5jWnS/juysLBa+8wxnXzucKnUacDjjL2JiY4sc+8KLetK5Z39ee+bvSa2nffgWp5zWmp5X3MAXU95k2odvccXNg8nYv4+3X3qKEY88T/WEJP5K31Os8y5I/QYNmfzRpwBkZWXRtXN7Ona+KCix8gr1Z81TVlYWj41/hFdfe4PExESuufJyOnTsRKPGwY+dVwTlt8KbqKr6PbAGqOguq91tvuqoqqcHI7kB1KyZwCnNWwAQH1+Bhg0bkpaaGoxQANSoWZNmpzR348VTv0FD0namMXf2d3S/tA8A3S/tw5xZMwMSr2ajlpSNr3jMtn1pW6nRqCUACU1PZ+vyHwBIXfsLlWvXp0qdBgCcEF8JiSl6gju55Rm5tbMcv/w0hwsv6gHAhRf1YMlPzkfhp9lfcVabjlRPcK5mVKpSrchxfbVwwY/UTU6mdm2vU2MGTKg/a55WrlhOcnI96iYnU6ZsWbp278HsAH3G/BUTIz4v4VZoghORK4CFQH/gCmCBiFwe7IIVxdatKaxZvZpWp54Wknjbtm1l3drVtGx5Knt276ZGzZqAkwT/3BOcGgxApVr12L7SmR83Zdl8DqbvAmD/zq0IMPeVscz891DWzvxfwGPvTd9DlWo1AKhSrQZ/pf8JwI5tm8nY/xePPzCIh+6+nvkzpwc8dl5ffTmdS7r1CHqc/IT6s5aWmkpSrb8vhSckJpIaouSaV6Bm1QoFXzoZRgNnq+oNqno9cA7wDx+Pr8DXIrI474zWOTxnvp40scD5YQt14EAGI++5m3vvf5AKFSoU+Tj+xBs1cijDRo4iPgTxPJ111d38Pm8aM58extFDB4mJda40aHYWuzb+yjnXjaD93U+ybcWPpK1bFpIyZWdlsWn9GoY//AwjH/0PUydPYsfWzUGLl5l5hDmzv6PLxV2DFqMgof6sAWg+8xuHK4FEUoLz5RpcjKqmeazvxvf5VC9Q1W0ikgB8IyJrVHWO5w6eM18fzPQ+S3VBMjMzGTHsbrr3uJTOXS4uyiH8cjQzk1Ejh3FJt5507NwFgGrVq7Nr505q1KzJrp07qVoteE20SonJtB30KOA0V3es/hmA8pVrULNRS06o4HRuJDVvzZ8pv5PQNHC1jMpVqpG+ZxdVqtUgfc8uKlWpCkDV6glUqFSFE8qV54Ry5Wna4gw2b/iNpDonBSy2p/lz59LslOZUr1EjKMcvSKg/azkSE5PYsX1H7npaaioJCeEZsLQE5C2f+ZKoZojIVyJyo4jcCEzDmZ+wUKq6zf2ZBnyCU/sLKFVl3NjRNGjYkAE3FDpNYkDijR/3D+o3aMg1A27M3d62fUemf+5c/J7++ae07dApaGU4tC/dKUt2Nmu++YCGbboBkNjsTPZu28TRI4fIzspi5/qVVEpM9nYov51+blvmfTsNgHnfTuOM89oBcOZ57Vi3ailZWUc5fOgQG9atonZy/YDG9jTjy2khb56G+rPmqUXLVmzevImUlC1kHjnCjOnTaN8xeJ8xbyKpBie+3L8rIv2ACwAB5qjqJz68Jx6n9rfP/f0b4BFVnVHQe4pSg/tlySJuuv5amjRpirjjVA0ZOpy27dr7fIxDmVmF7+Ra+sti7rh5AI2aNM0d+nrQ4GG0aHUao++/hx3bt5NUqxbjn3qWypWrFHq8woYsX/DWv9i1fgWHM/6iXMUqnNL1Go4ePsSG+U6Sqd3qfFr2vCH3w7R50SzWfPshIkLSKa1p1avgL2JhQ5a//OQY1qxYwv6/0qlUpRp9rh3IWee358UnHmTPzh1Uq5nEXaMeo0JFp8Y4/X9vM++bL5CYGNpd3ItL+lxd4LGLM2T5wYMH6d6lA1O//JaKFSsW/oY8ijpkeSA+a8X5zs+d8z1PPfEY2dlZ9Onbj9tuH+T3McrFUeys0/H5H3z+ns4a2iasWc6nBFekA4s0xKm1gdMUfk9Vx3t7T1GbqMXlT4ILNJuTIfRK85wMgUhwnV/40efv6cwh54f1jAu8Bici81T1QhHZB8ckHsGZZLpSAW8FZ4cNQGi6mIwxIRPO/yD8VWCCU9UL3Z/+twGMMVErgvKbT/fBve3LNmNM6RBJnQy+3CbSwnNFROKAs4JTHGNMSVcCHlDwmbdrcKOAB4HyIvJXzmbgCO59a8aY0qckPILlqwKbqKr6uHv97V+qWsldKqpqdVUdFcIyGmNKEPHjT7j5cqPvQhHJ7dMXkSoi0ieIZTLGlGAx4vsSbr4kuIdUdW/OiqqmAw8Fr0jGmJIsUJ0MIpIsIrNEZLWIrBKRoXleHykiKiI13HURkf+IyHoRWS4iZxZWVp+eRS3i+4wxUSiAnaNHgRGqukREKgKLReQbVf1VRJKBLoDniA3dgCbuci7wsvuzQL7U4BaJyDMi0khEGorIs8DiopyNMSbyxYj4vHijqttVdYn7+z5gNZAzuN+zwH0c+5BBb+AtdfwEVBGRWt5i+FITG4IzPNIHOL2oXwN3+fC+iBEXxosFdSoXbTjxQOjUf0zYYj/94siwxe7fqm7YYlcsH/mNH396Ud1h0jyHSpvgjiCUd7/6wBk44032Araq6rI8zdw6wBaP9RR32/aC4hf6t62qGcADhe1njCkd/Gmieg6HVvDxpALwP2AYTrN1NJDfWFT5Rfb6XKy3++CeU9VhIvJ5fgcpKZPOGGNCK5DPoopIGZzk9q6qfiwirYAGQE7trS6wRETOwamxeY7/VRfY5u343mpwOY9j/buIZTfGRKFApTdxMtgknHlengFQ1RVAgsc+m4DWqrpLRKYCg0VkMk7nwl5VLbB5Ct4ftl/s/vRnghljTJQL4DOmFwADgBUistTd9qCqFjSg7nSgO7AeOAAUOuqotybqCry0b1X11MIOboyJPoHqk1PVeRRSIVTV+h6/K352cHprovZ0f+YcMKfJei1O9jTGlEKR9CyqtybqHwAicoGqXuDx0gMiMh94JNiFM8aUPCVhGCRf+XKjb7yIXJizIiJtgPjgFckYU5JF0rOovtx1eAvwuvvAvQJ7gZuDWipjTIkVSTU4X270XQycJiKVcCap2VvYe4wx0Sty0ptvQ5Ynisgk4ANV3SsizUXklhCUzRhTAsXGiM9LuPnSRP0v8AbO4xMA63CeS50UpDL5Zcf27Yx58D5279qFxMTQ7/IruHbADUGLN27saObNmU3VatWY8vHnALz8f8/z/ezviImJoWrVajz86OPUDNCs49++/jQbly2gfKUqXPfo30+8LPv2M5bNnEpMbAz1Tz2XC6+4lTU/fseSGR/m7rMrZSNXP/QiNU9qVOT4MTHC/HfvY1vaXvoNfYU7rmzH4Gs60uikmtTteD+70zMAqFKxPK8+fB0N6tbg8JFMbn/4XX793es9mF59M8k57xMrVeG6f/593ktzzjsmhganOecNsHPLBr578z8cOZiBSAxXPfQCcWWK/5xv6o7t/POhUezZvRuJEXr17c8VVw/gt3Vr+Pfjj3DwwAGSatfmoUefIr5ChWLH82b+3Dk8+cR4srOy6duvP7fcNrDwNwVBVDVRgRqqOsUdwhxVPSoiPk0kKiJVgIlAS5zrdzer6o9FLm0+YuNiGXHvA5zSvAUZGfu5+op+nNfmAho1ahzIMLku7d2HK6++hrGj/348d8CNtzBosDOU1eR33+a1V1/iwX88HJB4p1xwMad27sXXE/+Vu23L6qVs+OUHrnnkZeLKlOXAX85M983O70Sz853ZznelbOSL/zxcrOQGMPiajqzdmErF+HIA/Lh0A9PnrOTriccM3cV9t1zCsrUpXDniNZrWT+S5B66g+x0vFDlu8wsv5rQCzvvaPOednZXFVxOe4pLb7qXmSY04uP8vYmJjixzbU2xcHIPvuY+TmzXnQEYGNw/oz9nnns+T/xzLXUPv5YyzzuaLzz7mvbdf57ZBdwckZn6ysrJ4bPwjvPraGyQmJnLNlZfToWMnGjUOzufcmwjKbz71omaISHXcm35F5DycjgZfPA/MUNVmOHOkri5SKb2oWTOBU5o78+LEx1egYcOGpKWmBjpMrjPPOptKlY6dsb6Cx//cBw8dDOgHoM7JrSgXf+zMjStmfcFZ3a/MraGcmKc8AOsWzKLpuR2KFzuhCl0vbMEbn/yQu23Z2hQ2b99z3L7NGiYxe+FaJ/amVOrVrkZCtaLPOFnn5FaUq3D8ebfO57z/WLmYGnUb5Cbz8hUqERMTmARXo0ZNTm7W3IkXH0/9+g3ZlZbG5j82cfqZrQE4+9zz+f67bwISryArVywnObkedZOTKVO2LF2792D2rJlBjVmQQA2XFAq+1OCGA1OBRu79bzWBywt7k9sp0Q64EUBVj+BMWBM0W7emsGb1alqdGvr5pl984Tmmf/4Z8RUq8OrEN4MaKz11K9t+W8mPH/+X2DJlaXvlbSQ2OPmYfdYtnMOlQx4uVpx/3duP0c9/SoUTyxW674p1W+nd+XR+WLqB1i3qcVKtatRJrELann3FKoOnP3dsZeu6lfzw8X+JK1OWC6+4jaSGJ5OemoKI8Mm/H+Tgvr00Pbc9rbtfEbC4ObZv28q6tatp3vJUGjZqwrzvZ9G2QydmffsVqak7Ah7PU1pqKkm1knLXExITWbF8eVBjFqQE5C2fea3BiUgMUA5oD7QBbgdaqKovf7MNgZ3AGyLyi4hMFJHj7p8TkYEiskhEFk2aWPTJug4cyGDkPXdz7/0PHlOjCpW7hgxj2tez6NbjUqZMfjeosbKzszicsZ8rxjzPhVfcypcvj8d5isWx4/c1lCl7AtXr1i9yjG5tW5K2Zx+/rN5S+M7Av9/4hioVT+SnyQ8w6Kr2LFubwtGs7CLHz49mZ3H4wH6uzHPe2VlZbPttJV1vv5/+Dz7N70t+YPOvvwQ09oEDGYy+bxhDRzxAfIUKjBr7KB9/+D43X9efAwcOUKZMmYDGy0vzeWoyXNfComZeVFXNFpGnVfV8YFURjn0mMERVF4jI8zjjyv0jT4zc8aIOZnof26kgmZmZjBh2N917XErnLvkNIxU6Xbv1YOjgO7j9ziFBi1Ghag0anXUBIkJSw2YgMRzctze3ybZu4exiN0/PP70hPdu3ouuFLTihbBkqxZfj9X9ez81j3sp3/30Zh7j94Xdy19dMG8emrbuLVYa8KlStQWOP8xb3vCtUq0mdk0+lfEVnbqT6p57Nzj/Wc1LzMwIS9+jRTMbcN4yLu/agfacuANSr35BnX3wNgM1/bOLHecEdkyIxMYkd2/+uJaalppIQoI4sf8WWgMTlK1+uwX0tIv3E/3ScAqSo6gJ3/SOchBdQqsq4saNp0LAhA24odHCBoNj8x6bc37+fPYv6DRoGNV6jM9qQstoZfOHPHSlkH83M/XJrdja/LZpL03M6FCvG2Bem0rjrP2jW4yGuf+ANZv+8rsDkBlC5QnnKxDnXvW7q24Z5S9azL+NQscqQV8Mz27DF47yz3POu1/Isdm3ZSObhQ2RnZbF17XKq1T4pIDFVlccfGUu9Bg256robc7f/ucdJ3tnZ2bw56VV697syIPEK0qJlKzZv3kRKyhYyjxxhxvRptO/YKagxCxJtTzIMx3k066iIHMK5z09VtZK3N6nqDhHZIiInq+paoDPwa7FLnMfSXxbzxeef0aRJU67o1xuAIUOH07Zd+0CHAuDB+0eweNFC0tPT6d6lAwMHDWb+vDn8sWkjMTEx1KpVm1FjHg5YvBmvPE7K2uUc2r+XSSOu5bzeA2je9hK+ff0Z3vnHQGJjy9Dl1ntzmwNb162gQtUaVE7wOlR9kd15dXuG33ARidUr8fOUB5kxbxV3PvJFP6EnAAAU2klEQVQezRomMfHRAWRlZbNmww7uGFe8ZvqXrzxOyhr3vIdfy7l9BtCi7SV8M+kZ3hkzkJjYMlzsnne5+IqcecllTH5kCCJC/VPPocFpXuci8dnyZUv4avpUGjVuyo3XXAbA7XcOY8uWP/j4w/cBaN/xInr06huQeAWJi4tj1OixDBp4K9nZWfTp24/GjZsENWZBSkLi8pV4XrsJ+MFFTse5TaQssAG4SVX/LGj/ojZRiyvQ14r88dbizYXvFCQjB4dvLFObkyE8ysUV/0GEEZ+v9fl7+vSlJ4c1HXobDy4BeBBoDCwHnlDVv/w5uKouBVoXq4TGmBIlkmpw3q7BvQVkAC8AFYH/hKRExpgSTcT3Jdy81ZeTVDXn8ayvRGRJKApkjCnZ4kpC5vKRtwQnIlKVvwcPiPVcV9Xjb2c3xkS9CMpvXhNcZZwZ7D1PJ6cWpzg38hpjSpmS8AiWr7wNWV4/hOUwxkSICMpvPt0HZ4wxuSKpF9USnDHGLyVhIEtfWYIzxvglgvKb1xt9q3l7o/WiGlM6SQTNyuCtBrcYp7c0v7MJSi9quC5elonzZcyB4Ljl3Pphi33VvOfCFjvjsE+DQgdFuTLh+/eOBlFRg1PVBqEsiDEmMkRSgvNlVi0RketE5B/u+kkick7wi2aMKYkiacBLX+rqLwHnA9e46/uAF4NWImNMiRYb4/sSbr70op6rqmeKyC8AqvqniBR/PjZjTESKiicZPGSKSCx/z6pVEwjfAGrGmLCKqmtwOMMkfQIkiMh4YB7wWFBLZYwpsaJluCQAVPVdEVmMM+S4AH1UNeDzmxpjIkNMBN0HV2ANTkSq5SxAGvA+8B6QWthNwMaY6BXIGpyIvC4iaSKyMs/2ISKyVkRWichTHttHich697VLCju+rzf6ngT86f5eBdgM2H1yxpRCcYG9CPdf4P9wRhAHQEQ6Ar2BU1X1sDt9AiLSHLgKaAHUBr4VkaaqWuBd4wXW4FS1gao2BL4CLlXVGqpaHegJfFzs0zLGRKRA1uBUdQ6Q97HPQThzwBx290lzt/cGJqvqYVXdCKwHvN6T60snw9mqOt2jQF/izHRvjCmFYkR8XkRkoIgs8lgG+hCiKdBWRBaIyPcicra7vQ6wxWO/FHdbgXy5TWSXiIwB3sFpsl4HBHbK8mKaP3cOTz4xnuysbPr2688tt/nyd1h8Y8eMYs73s6lWrToff/ZFSGKGK3bqju08OnYUu3ftJiZG6HVZf668ZkDu6++99Qb/99y/mT5zHlWqVg14/KysLO666Wpq1Ezgn0//H/fccQMHDhwAIP3PPTRr3pJxTz4f8Ljjxo5m3pzZVK1WjSkffw7Ay//3PN/P/o6YmBiqVq3Gw48+Ts0gzzIfzs9aXv70jqrqBGCCnyHigKrAecDZwBQRaUjBz8UXyJca3NVATZxbRT4FEtxtXonIySKy1GP5S0SG+RDPL1lZWTw2/hFeemUin0ydxozpX/D7+vWBDpOv3n0u4+VXJ4YkVrhjx8bGMeSe+3j/48+Z8Ob7fDzlfTZucP6eU3dsZ+FPP5CYFJzJpgE+mfIuJ9X/+7Lvs6+8yatvfcirb31I85ancmH7zkGJe2nvPrzw8rHfzwE33sLkjz7jvSmf0LZdB1579aWgxPYUzs9aXjF+LEWUAnysjoU4993WcLcne+xXF9hWWFm9UtU9qjoUp1naVlWH+jJUkqquVdXTVfV04CzgAE6SDKiVK5aTnFyPusnJlClblq7dezB71sxAh8nXWa3PplLlyiGJFe7YNWrW5ORTmgMQHx9PvQYN2ZnmXBp5/uknuWvYiKA9e7gzbQcL5s+hW6/LjnvtQEYGSxcvpE37TkGJfeZZZ1OpUpVjtlWoUCH394OHDobkfq9wftby8qeJWkSfAp0ARKQpzsTxu4CpwFUicoKINACaAAu9HajQJqqItMLp4ajmru8CblDVlV7feKzOwO+q+ocf7/FJWmoqSbWSctcTEhNZsXx5oMMYD9u3beW3tatp0fJU5n7/HTUTEmnStFnQ4r383FPcNng4Bw9kHPfa/O9nckbrc4mPr5DPO4PnxReeY/rnnxFfoQKvTnwzpLHDLZCPaonI+0AHoIaIpAAPAa8Dr7u3jhzByTcKrBKRKcCvwFHgLm89qOBbLfJVYLiq1lPVesAI/G9TX4VzH91xPC9CTnrN38OC5tMELwmjGESrAwcyeHDkMIaOeIDY2FjenDSB2+4YHLR4P837nipVq9G0WfN8X5/1zZd07NItaPELcteQYUz7ehbdelzKlMnvhjx+OIkfS2FU9WpVraWqZVS1rqpOUtUjqnqdqrZU1TNV9TuP/ceraiNVPdnt8PTKlwQXr6qzPALMBuJ9eB8A7oP5vYAP83tdVSeoamtVbV2UzoHExCR2bN+Ru56WmkpCkC/4llZHMzN5cOQwLu7egw6du7A1ZQvbtm7l+qsu47IeXdiZlspN117O7l07AxZz1fKl/Dh3Ntf17cr4f9zH0sULeeLhUQD8tTedNb+u5Nw27QIWz19du/Vg5rdfhy1+OETVo1rABncsuLfd9euAjX7E6AYsUdVUfwvnixYtW7F58yZSUraQmJDIjOnTePxfTwcjVKmmqjz2yFjqN2jI1dfdCECjJk2ZPnNu7j6X9ejC6+9MCWgv6i13DuWWO4cCsGzJz3z47ps88PDjAHw/82vOu6AdZU84IWDxfLH5j02cVK++U4bZs6jfoHRNERxJLSRfEtzNwDicm3sFmAPc5EeMqymgeRoIcXFxjBo9lkEDbyU7O4s+ffvRuHGTYIU7xv0jh7Po54Wkp/9Jl07tGHTXEC7r1z8qYy9fuoQZ06bSqHFTbrjKudh/++BhtLkwfLWn2d/O4KoBNwc1xoP3j2DxooWkp6fTvUsHBg4azPx5c/hj00ZiYmKoVas2o8Y8HNQyQHg/a3mVgGHefCbOtbsgHVzkRJwb8xqq6t7C9j901Ps9LSawMg4fDWPs8M3JUPXEMmGLHc75PwDKxRX/SfkPl27z+Xva//TaYa3ueZtVa6q3N6pqr8IOrqoHgOpFKJcxpoSKlibq+Ti1r/eBBfjWKWKMiXKR1ET1luCSgC4419CuAaYB76vqqlAUzBhTMkVSDc7baCJZqjpDVW/AeSZsPTBbRIaErHTGmBInkPfBBZvXXlQROQHogVOLq48zfLkNlWRMKRYbQTU4b50MbwItgS+BcX4+mmWMiVIRlN+81uAGABk4YzPd7dHuFkBVtVKQy2aMKYGkRDQ+fVNgglPVSOosMcaESLTU4Iwx5jiRNKuWJThjjF+sBmciQvwJ4fvnD2dsUzyBHA8u2OxTZozxS2BnDQwuS3DGGL9ERS+qMcbkJ4JaqJbgjDH+sRqcMSZq2TU4Y0zUsl5UY0zUipz0ZgnOGOMnq8EZY6JW5KQ3S3DGGH9FUIazBGeM8UskNVGjYkik+XPn0KvHJfTs2oVJr02w2BbbYgdRJA1ZHtQEJyL3iMgqEVkpIu+LSLlAx8jKyuKx8Y/w0isT+WTqNGZM/4Lf168PdBiLbbFLbezjRFCGC1qCE5E6wN1Aa1VtCcQCVwU6zsoVy0lOrkfd5GTKlC1L1+49mD1rZqDDWGyLXWpj5yV+/Am3YDdR44DyIhIHnAhsC3SAtNRUkmol5a4nJCaSmpoa6DAW22KX2th5ifi+hFvQEpyqbgX+DWwGtgN7VfXrvPuJyEARWSQii4pyXUHR47aFat5Gi22xS0Ps4+L6sYRb0HpRRaQq0BtoAKQDH4rIdar6jud+qjoBmABw6Gg+/4qFSExMYsf2HbnraampJCQkFKfoFttiW2wvomLi5wC4CNioqjtVNRNnPtU2gQ7SomUrNm/eRErKFjKPHGHG9Gm079gp0GEstsUutbHzCmQTNb+OSBFpICILROQ3EflARMoWtazBvA9uM3CeiJwIHAQ6A4sCHSQuLo5Ro8cyaOCtZGdn0advPxo3bhLoMBbbYpfa2HkFqv7m0RHZXFUPisgUnI7I7sCzqjpZRF4BbgFeLlIMVb9bhb4fXGQccCVwFPgFuFVVDxe0f1GaqMYY35WLK35+WrZln8/f09OSKxYYz01wPwGnAX8BnwIvAO8CSap6VETOBx5W1UuKUtagPsmgqg8BDwUzhjEmtPy5/UNEBgIDPTZNcK+7o6pbRSSnI/Ig8DWwGEhX1aPu/ilAnaKW1R7VMsb4xZ8+Bs9OxOOPc3xHJNAtv8P4XUiXJThjjF8C2Ima2xHpHFdyOiKriEicW4urSzHun42KZ1GNMaETwCcZcjsixbn3pDPwKzALuNzd5wbgs6KW1RKcMcYvgbpNRFUXAB8BS4AVOPloAnA/MFxE1gPVgUlFLmswe1H9Zb2oxgRXIHpRV2/L8Pl7ekrt+LDeFWzX4Iwx/omcBxkswRlj/BNJA15agjPG+CVy0pslOGOMvyIow1mCM8b4pSQMZOkrS3DGGL9E0CU4S3DGGP9EUH6zBGeM8U8kDXhpCc4Y45cIym+W4Iwx/omg/GYJzhjjpwjKcJbgjDF+sdtEjDFRy67BGWOiVowlOGNM9IqcDGcJzhjjl0hqokbFiL7z586hV49L6Nm1C5Ney3d+C4ttsS12gIgfS9ipatAWYCiwElgFDCts/4OZqv4u+w8d1U6dO+tvGzbrXxmHtWfPS3Xl6t/8Po7FttilIXYgvtfb0g+rr0sw84svS9BqcCLSErgNOAdnYteeIhLwqbhXrlhOcnI96iYnU6ZsWbp278HsWTMDHcZiW+xSGzsvEfF5CbdgNlFPAX5S1QPu9F/fA30DHSQtNZWkWkm56wmJiaSmpgY6jMW22KU2dl6R1EQNZoJbCbQTkeoiciLQHUjOu5OIDBSRRSKyqCjXFTSfeWpC9T+HxbbYpSH28XEDM6tWKAStF1VVV4vIk8A3wH5gGXA0n/1yZ74uyqxaiYlJ7Ni+I3c9LTWVhISEohbbYltsi12ISHqSIai9qKo6SVXPVNV2wB7gt0DHaNGyFZs3byIlZQuZR44wY/o02nfsFOgwFttil9rYx4mgNmpQ74MTkQRVTRORk4DLgPMDHSMuLo5Ro8cyaOCtZGdn0advPxo3DnhfhsW22KU2dl4lIG/5LKgTP4vIXJyZqTOB4arqtdvHJn42JrgCMfHznowsn7+n1eJjo3fiZ1VtG8zjG2NCryR0HvgqKp5kMMaY/NizqMYYv0RSDc4SnDHGL5F0m4glOGOMX6wGZ4yJWpbgjDFRy5qoxpioFUk1OLtNxBjjl0A+qSUiXUVkrYisF5EHAl1WS3DGGP8EKMOJSCzwItANaA5cLSLNA1lUa6IaY/wSE7g26jnAelXdACAik4HewK+BClCiElxxnpMTkYHu0EshZ7EtdmmIncOf76mIDAQGemya4FH+OsAWj9dSgHOLX8K/RVMTdWDhu1hsi22xQ0lVJ6hqa4/FMznnlygDOuBGNCU4Y0xkSeHYUb7rAtsCGcASnDEmXH4GmohIAxEpC1wFTA1kgBJ1Da6YwnldwmJb7NIQO6BU9aiIDAa+AmKB11V1VSBjBHXAS2OMCSdrohpjopYlOGNM1Ir4BCci5URkoYgsE5FVIjIuxPFjReQXEfkilHHd2JtEZIWILBWRRSGOXUVEPhKRNSKyWkQCPqFQAXFPds83Z/lLRIaFIrYb/x73c7ZSRN4XkXIhjD3UjbsqlOccySL+Gpw4s9/Gq+p+ESkDzAOGqupPIYo/HGgNVFLVnqGI6RF7E9BaVXeFMq4b+01grqpOdHvATlTV9BCXIRbYCpyrqn+EIF4dnM9Xc1U9KCJTgOmq+t8QxG4JTMa5+/8IMAMYpKoBn4ozmkR8DU4d+93VMu4SkqwtInWBHsDEUMQrKUSkEtAOmASgqkdCndxcnYHfQ5HcPMQB5UUkDjiRAN+35cUpwE+qekBVjwLfA31DFDtiRXyCg9xm4lIgDfhGVReEKPRzwH1Adoji5aXA1yKy2H0kJlQaAjuBN9zm+UQRiQ9h/BxXAe+HKpiqbgX+DWwGtgN7VfXrEIVfCbQTkeoiciLQnWNvkjX5iIoEp6pZqno6zp3Q57jV+aASkZ5AmqouDnYsLy5Q1TNxRmO4S0TahShuHHAm8LKqngFkAAEf6sYbt1ncC/gwhDGr4jwM3gCoDcSLyHWhiK2qq4EngW9wmqfLgKOhiB3JoiLB5XCbSbOBriEIdwHQy70ONhnoJCLvhCBuLlXd5v5MAz7BuT4TCilAikdN+SOchBdK3YAlqpoawpgXARtVdaeqZgIfA21CFVxVJ6nqmaraDtgD2PW3QkR8ghORmiJSxf29PM6HcE2w46rqKFWtq6r1cZpK36lqSP43BxCReBGpmPM7cDFOMyboVHUHsEVETnY3dSaAQ9z46GpC2Dx1bQbOE5ET3c6tzsDqUAUXkQT350nAZYT+/CNONDyqVQt40+1RiwGmqGrIb9kIg0TgE+d7RhzwnqrOCGH8IcC7blNxA3BTqAK716C6ALeHKiaAqi4QkY+AJTjNw18I7aNT/xOR6kAmcJeq/hnC2BEp4m8TMcaYgkR8E9UYYwpiCc4YE7UswRljopYlOGNM1LIEZ4yJWpbgDAAi0ldEVESaFbLfjSJSuxhxOoRj5BVTOlmCMzmuxhkp46pC9rsR5zElY0o8S3AGEamA8+jZLXgkOBG5zx1vbpmIPCEil+MMDfWuOxZbeXdMuhru/q1FZLb7+zki8oP7MP4PHk89GBMy0fAkgym+PsAMVV0nIntE5EycJyX64Iy1dkBEqqnqHneSkJGqughACp7lfA3Qzp1Y5CLgMaBf8E/FmL9ZgjPgNE+fc3+f7K7HAG+o6gEAVd3j5zEr4zxC1wRnWKcyASqrMT6zBFfKuc82dgJaiojiTN+mwP/wbeDQo/x9qcNz+O5HgVmq2ldE6uOM8mJMSNk1OHM58Jaq1lPV+qqaDGzEGY7nZvfBdkSkmrv/PqCix/s3AWe5v3s2QSvjDCcOTseEMSFnCc5cjTOWnKf/4fSUTgUWuaMlj3Rf+y/wSk4nAzAOeF5E5gJZHsd4CnhcRObj1AqNCTkbTcQYE7WsBmeMiVqW4IwxUcsSnDEmalmCM8ZELUtwxpioZQnOGBO1LMEZY6LW/wNYWzcOZGb66gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from ml_utils.classification import confusion_matrix_visual\n",
    "\n",
    "confusion_matrix_visual(y_test, preds, np.sort(y_test.unique()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extend the plot_roc() function to multi-class classification problems"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import auc, roc_curve\n",
    "\n",
    "def plot_multi_class_roc(y_test, preds):\n",
    "    \"\"\"\n",
    "    Plot ROC curve to evaluate classification.\n",
    "\n",
    "    Parameters: \n",
    "        - y_test: The true values for y\n",
    "        - preds: The predicted values for y as probabilities\n",
    "\n",
    "    Returns:\n",
    "        ROC curve.\n",
    "    \"\"\"\n",
    "    fig, ax = plt.subplots(1, 1)\n",
    "    \n",
    "    ax.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--', label='baseline')\n",
    "    \n",
    "    class_labels = np.sort(y_test.unique())\n",
    "    for i, class_label in enumerate(class_labels):\n",
    "        actuals = np.where(y_test == class_label, 1, 0)\n",
    "        predicted_probabilities = preds[:,i]\n",
    "        fpr, tpr, thresholds = roc_curve(actuals, predicted_probabilities)\n",
    "        auc_score = auc(fpr, tpr)\n",
    "        ax.plot(fpr, tpr, lw=2, label=f\"\"\"class {class_label}; AUC: {auc_score:.2}\"\"\")\n",
    "\n",
    "    plt.legend()\n",
    "    plt.title('Multi-class ROC curve')\n",
    "    plt.xlabel('False Positive Rate (FPR)')\n",
    "    plt.ylabel('True Positive Rate (TPR)')\n",
    "    \n",
    "    return ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x13fd5d0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_multi_class_roc(y_test, lm_pipeline.predict_proba(X_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extend the plot_pr_curve() function to multi-class classification problems"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import auc, average_precision_score, precision_recall_curve\n",
    "\n",
    "def plot_multi_class_pr_curve(y_test, preds):\n",
    "    \"\"\"\n",
    "    Plot precision-recall curve to evaluate classification.\n",
    "\n",
    "    Parameters: \n",
    "        - y_test: The true values for y\n",
    "        - preds: The predicted values for y as probabilities\n",
    "\n",
    "    Returns:\n",
    "        Plotted precision-recall curve.\n",
    "    \"\"\"\n",
    "    class_labels = np.sort(y_test.unique())\n",
    "\n",
    "    row_count = np.ceil(len(class_labels) / 3).astype(int)\n",
    "    fig, axes = plt.subplots(row_count, 3, figsize=(15, row_count*5))\n",
    "    axes = axes.flatten()\n",
    "    \n",
    "    if len(axes) > len(class_labels):\n",
    "        for i in range(len(class_labels), len(axes)):\n",
    "            fig.delaxes(axes[i])\n",
    "\n",
    "    for i, class_label in enumerate(class_labels):\n",
    "        axes[i].axhline(sum(y_test == class_label)/len(y_test), color='navy', lw=2, linestyle='--', label='baseline')\n",
    "        actuals = np.where(y_test == class_label, 1, 0)\n",
    "        predicted_probabilities = preds[:,i]\n",
    "        precision, recall, thresholds = precision_recall_curve(actuals, predicted_probabilities)\n",
    "        auc_score = auc(recall, precision)\n",
    "        ap_score = average_precision_score(actuals, predicted_probabilities)\n",
    "        axes[i].plot(recall, precision, lw=2, label=f\"\"\"AUC: {auc_score:.2}; AP : {ap_score:.2}\"\"\")\n",
    "\n",
    "        axes[i].legend()\n",
    "        axes[i].set_title(f'Precision-recall curve: class {class_label}')\n",
    "        axes[i].set_xlabel('Recall')\n",
    "        axes[i].set_ylabel('Precision')\n",
    "\n",
    "        axes[i].set_xlim(-0.05, 1.05)\n",
    "        axes[i].set_ylim(-0.05, 1.05)\n",
    "\n",
    "    return axes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note how these tell a much different story than the ROC curves, since we have a class imbalance:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x0147C570>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x014A80B0>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x014C1110>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x014DA1D0>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x014F6270>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x12771330>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x1278C3F0>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x127A9AB0>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x127C1590>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 7 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_multi_class_pr_curve(y_test, lm_pipeline.predict_proba(X_test))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
